msAggr_seurat
Creating new pipeline using seurat v4.0.2 available 2021.06.23
Important notes:
- FILTERING on
percent.mt, but NOT regressing onpercent.mt - Regressing on
nCounts_RNAandnFeature_RNA
Setup
Load libraries required for Seuratv4
knitr::opts_knit$set(root.dir = "~/Desktop/10XGenomicsData/msAggr_scRNASeq/IndividualPops/")
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(clustree)Loading required package: ggraphSource functions
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/read_10XGenomics_data.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/PercentVariance.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/ColorPalette.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/Mouse2Human_idconversion.R")A note about using SCTransform versus ScaleData
https://bioconductor.org/packages/3.10/workflows/vignettes/simpleSingleCell/inst/doc/batch.html#62_for_gene-based_analyses >You can also normalize and scale data for the RNA assay. There are numerous resources on this, but Aaron Lun describes why the original log-normalized values should be used for DE and visualizations of expression quite well here: > >For gene-based procedures like differential expression (DE) analyses or gene network construction, it is desirable to use the original log-expression values or counts. The corrected values are only used to obtain cell-level results such as clusters or trajectories. Batch effects are handled explicitly using blocking terms or via a meta-analysis across batches. We do not use the corrected values directly in gene-based analyses, for various reasons: > >It is usually inappropriate to perform DE analyses on batch-corrected values, due to the failure to model the uncertainty of the correction. This usually results in loss of type I error control, i.e., more false positives than expected. > >The correction does not preserve the mean-variance relationship. Applications of common DE methods like edgeR or limma are unlikely to be valid. > >Batch correction may (correctly) remove biological differences between batches in the course of mapping all cells onto a common coordinate system. Returning to the uncorrected expression values provides an opportunity for detecting such differences if they are of interest. Conversely, if the batch correction made a mistake, the use of the uncorrected expression values provides an important sanity check. > >In addition, the normalized values in SCT and integrated assays don’t necessary correspond to per-gene expression values anyway, rather containing residuals (in the case of the scale.data slot for each). ## Set global variables
projectName <- "LSKSubpop"
jackstraw.dim <- 40Store session info
sessionInfo.filename <- paste0(projectName, "_sessionInfo.txt")
sink(sessionInfo.filename)
sessionInfo()
sink()setwd("~/Desktop/10XGenomicsData/cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = c("LSKm2"))
data.object<-Read10X(data_file.list)seurat.object<- CreateSeuratObject(counts = data.object, min.cells = 3, min.genes = 200, project = projectName)Clean up to free memory
remove(data.object)Add mitochondrial metadata and plot some basic features
seurat.object[["percent.mt"]] <- PercentageFeatureSet(seurat.object, pattern = "^mt-")
VlnPlot(seurat.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
plot1 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
remove low quality cells require: nFeature_RNA between 200 and 4000 (inclusive) require: percent.mt <= 5
print(paste("original object:", nrow(seurat.object@meta.data), "cells", sep = " "))[1] "original object: 11187 cells"seurat.object <- subset(seurat.object,
subset = nFeature_RNA >=200 &
nFeature_RNA <= 4000 &
percent.mt <= 5
)
print(paste("new object:", nrow(seurat.object@meta.data), "cells", sep = " "))[1] "new object: 10627 cells"Normalization
Struggling to wrap my head around this one. It seems that SCTransform is best for batch correction, but NormalizeData and ScaleData are best for DGE. Several vignettes have performed both
`selection.method
How to choose top variable features. Choose one of :
vst: First, fits a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then standardizes the feature values using the observed mean and expected variance (given by the fitted line). Feature variance is then calculated on the standardized values after clipping to a maximum (see clip.max parameter).
mean.var.plot (mvp): First, uses a function to calculate average expression (mean.function) and dispersion (dispersion.function) for each feature. Next, divides features into num.bin (deafult 20) bins based on their average expression, and calculates z-scores for dispersion within each bin. The purpose of this is to identify variable features while controlling for the strong relationship between variability and average expression.
dispersion (disp): selects the genes with the highest dispersion values`
seurat.object <- NormalizeData(seurat.object, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|Find variable features
seurat.object <- FindVariableFeatures(seurat.object, selection.method = "vst", nfeatures = 2000)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|top10 <- head(VariableFeatures(seurat.object), 10)
plot1 <- VariableFeaturePlot(seurat.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
Scale data (linear transformation)
all.genes <- rownames(seurat.object)
seurat.object <- ScaleData(seurat.object, features = all.genes, vars.to.regress = c("nCount_RNA", "nFeature_RNA"))Regressing out nCount_RNA, nFeature_RNA
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# save.image(file = paste0(projectName, '.RData'))
saveRDS(seurat.object, file = paste0(projectName, "_raw.RDS"))PCA
linear dimensional reduction. Default are based on VariableFeatures, but can be changed
seurat.object <- RunPCA(seurat.object, features = VariableFeatures(object = seurat.object))Plot results
VizDimLoadings(seurat.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 2)
DimPlot colored by orig.ident
DimPlot(seurat.object, reduction = "pca", group.by = "orig.ident")
Let’s put in a concerted effort to pick the right dimensionality using the newest software
# jackstraw.dim <- 40
# seurat.object <- JackStraw(seurat.object, num.replicate = 100, dims = jackstraw.dim) #runs ~50 min
# seurat.object <- ScoreJackStraw(seurat.object, dims = 1:jackstraw.dim)
# save.image(paste0(projectName, ".RData"))Draw dim.reduction plots
# JackStrawPlot(seurat.object, dims = 25:36)ElbowPlot(seurat.object, ndims = 50)
percent.variance(seurat.object@reductions$pca@stdev)Number of PCs describing X% of variance
ElbowPlot(seurat.object, ndims = 50)
percent.variance(seurat.object@reductions$pca@stdev)
Percent of PCs describing X% of variance (transcribed from above cell because I don’t know how to freeze results)
Num pcs for 80% variance: 12 Num pcs for 85% variance: 18 Num pcs for 90% variance: 26 Num pcs for 95% variance: 37
ID clusters based on different variances
Describe 80% of variance with 12 dimensions
Neighborhood and umap
set total.var <- 80%
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance: ", length(which(cumsum(tot.var) <= 80)))[1] "Num pcs for 80% variance: 25"paste0("Num pcs for 85% variance: ", length(which(cumsum(tot.var) <= 85)))[1] "Num pcs for 85% variance: 30"paste0("Num pcs for 90% variance: ", length(which(cumsum(tot.var) <= 90)))[1] "Num pcs for 90% variance: 37"paste0("Num pcs for 95% variance: ", length(which(cumsum(tot.var) <= 95)))[1] "Num pcs for 95% variance: 43"Plot UMAP
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 80))
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)Computing nearest neighbor graphComputing SNNseurat.object <- FindClusters(seurat.object, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8185
Number of communities: 11
Elapsed time: 2 secondsseurat.object <- RunUMAP(seurat.object, dims = 1: ndims)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
16:00:37 UMAP embedding parameters a = 0.9922 b = 1.112
16:00:37 Read 10627 rows and found 25 numeric columns
16:00:37 Using Annoy for neighbor search, n_neighbors = 30
16:00:37 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:00:38 Writing NN index file to temp file /var/folders/4f/fwrj6fnn1dn4g8wsf0zv563hjsvl24/T//RtmpFboagN/filed9154a56740a
16:00:38 Searching Annoy index using 1 thread, search_k = 3000
16:00:43 Annoy recall = 100%
16:00:43 Commencing smooth kNN distance calibration using 1 thread
16:00:44 Initializing from normalized Laplacian + noise
16:00:45 Commencing optimization for 200 epochs, with 456676 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:00:55 Optimization finishedfor(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8185
Number of communities: 11
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7648
Number of communities: 17
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7276
Number of communities: 21
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6988
Number of communities: 24
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10627
Number of edges: 352668
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6750
Number of communities: 31
Elapsed time: 2 secondsfor (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}
Evaluate cluster stability
saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
Please use the `.add` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')Describe 85% of variance with 18 dimensions
Neighborhood and umap
set total.var <- 85%
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}for (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}Generate statistics for each cluster/resolution combo
Evaluate cluster stability
Must ensure we have the right cluster stability, that is, cells that start in the same cluster tend to stay in the same cluster. If your data is over-clustered, cells will bounce between groups.
Following [this tutorial by Matt O.].https://towardsdatascience.com/10-tips-for-choosing-the-optimal-number-of-clusters-277e93d72d92. Previously my favourite has been Clustree, which gives a nice visual NB: For some reason clustree::clustree() didn’t work, whereas library(clustree) followed by clustree() did.
Describe 90% of variance with 26 dimensions
Neighborhood and umap
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}for (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = "RNA_snn_res", x = meta.col)==TRUE | grepl(pattern = "orig.ident", x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
pt.size = 1,
cols = color.palette) +
ggtitle(paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col) ))
plot(myplot)
png(filename = paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col), "-umap.png"), height = 800, width = 800)
plot(myplot)
dev.off()
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
pt.size = 1,
cols = color.palette) +
facet_wrap(meta.col) +
ggtitle(paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col)))
png(filename = paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col), "-umap_FacetRes.png"), height = 800, width = 800)
plot(myplot)
dev.off()
}
}saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))Generate statistics for each cluster/resolution combo
current_res <- 'RNA_snn_res.1'
cluster_ids <- sort(unique(seurat.object@meta.data[,current_res]))
counts_df <- data.frame(matrix(nrow = length(cluster_ids), ncol = 9))
rownames(counts_df) <- cluster_ids
colnames(counts_df) <- c("LSKm2", "CMPm2", "MEPm", "GMPm", "TotInClust", "PctClusLSK", "PctClusCMP", "PctClusMEP", "PctClusGMP")
for(id in cluster_ids){
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "LSKm2"),])
counts_df[id, "LSKm2"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "CMPm2"),])
counts_df[id, "CMPm2"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "MEPm"),])
counts_df[id, "MEPm"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "GMPm"),])
counts_df[id, "GMPm"] = cell_value
}
counts_df["TotInClust"] <- rowSums(counts_df, na.rm = TRUE)
counts_df$PctClusLSK <- round(counts_df$LSKm2/counts_df$TotInClust, 3)*100
counts_df$PctClusCMP <- round(counts_df$CMPm2/counts_df$TotInClust, 3)*100
counts_df$PctClusMEP <- round(counts_df$MEPm/counts_df$TotInClust, 3)*100
counts_df$PctClusGMP <- round(counts_df$GMPm/counts_df$TotInClust, 3)*100
xlsx::write.xlsx(x = counts_df,
file = paste0(projectName, "CellCountPerClust",ndims, "res.1.xlsx"),
sheetName = "res1",
col.names = TRUE,
row.names = TRUE,
append = TRUE)
counts_df
Evaluate cluster stability
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')
png(filename = paste0(projectName, "_dim", ndims, "-clustree.png"), height = 800, width = 1600)
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')
dev.off()clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'data', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')Describe 95% of variance with 37 dimensions
Neighborhood and umap
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))
seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
seurat.object <- FindClusters(seurat.object, resolution = x)
}for (meta.col in colnames(seurat.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(seurat.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))Generate statistics for each cluster/resolution combo
current_res <- 'RNA_snn_res.0.5'
cluster_ids <- sort(unique(seurat.object@meta.data[,current_res]))
counts_df <- data.frame(matrix(nrow = length(cluster_ids), ncol = 4))
rownames(counts_df) <- cluster_ids
colnames(counts_df) <- c("LSKm2", "CMPm2", "MEPm", "GMPm")
for(id in cluster_ids){
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "LSKm2"),])
counts_df[id, "LSKm2"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "LSKm2"),])
counts_df[id, "CMPm2"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "MEPm"),])
counts_df[id, "MEPm"] = cell_value
cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) &
(seurat.object@meta.data$orig.ident == "GMPm"),])
counts_df[id, "GMPm"] = cell_value
}
Evaluate cluster stability
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'data', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") +
scale_color_continuous(low = 'red3', high = 'white')DGE
I think clusters from 90% variance at 0.5 and 1.0 resolution are most stable. We’ll do some statistics and DGE on that. Will also need to go back and play with SCTransform, since these are multiple cell types from multiple lanes. ## Load favourite dim reduction file
rds.file <- "msAggr_seurat_dim26.RDS"
seurat.object <- readRDS(rds.file)
ndims <- as.numeric(gsub("[^0-9]", "", stringr::str_split(rds.file, "_")[[1]][3]))Set favourite resolution
object.res <- ".0.5"
Idents(seurat.object) <- paste0("RNA_snn_res", object.res)
length(levels(seurat.object@active.ident))count number of filtered cells left from each population
# Number of filtered cells left in each pop
sapply(c("LSKm2", "CMPm2", "MEPm", "GMPm"), function(x) (c(nrow(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x,]))))par(mfrow = c(2, 2))
for (x in c("LSKm2", "CMPm2", "MEPm", "GMPm")){
h = hist(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x, 'percent.mt'], breaks = 30, plot = FALSE)
h$density = h$counts/sum(h$counts)*100
plot(h,freq=FALSE, main = paste(x, "percent mitoC"), xlab = "percent mitoC", ylab = "Frequency")
}
par(mfrow = c(1,1))VlnPlot(subset(seurat.object, subset = orig.ident == "MEPm"),
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1, pt.size = 0, fill.by = 'ident', flip = TRUE)DGE w/ resolution = 0.5
Strt with comparing all clusters against all other clusters and write out cluster info calculate FindAllMarkers() for different idents and save to new file
# ident.list <- colnames(seurat.object@meta.data)[grepl("^RNA_snn", colnames(seurat.object@meta.data))]
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1")
for(tested.ident in ident.list){
Idents(seurat.object) <- tested.ident
all.markers <- FindAllMarkers(seurat.object)
xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")],
file = paste0(projectName, "_FindALLMarkers_dim",ndims, "_allres.xlsx"),
sheetName = tested.ident,
col.names = TRUE,
row.names = FALSE,
append = TRUE)
}Create FindAllMarkers() lists for GSEA
object.res.allmarkers <- FindAllMarkers(seurat.object)Map HGNC symbols
Mouse2HumanTable <- Mouse2Human(object.res.allmarkers$gene)
HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(object.res.allmarkers$gene, Mouse2HumanTable$MGI)])
head(object.res.allmarkers)
object.res.allmarkers$HGNC <- HGNC
tail(object.res.allmarkers)
sig.res <- object.res.allmarkers[object.res.allmarkers$p_val_adj <= 0.05, ]
sig.res <- sig.res[c("avg_log2FC", "HGNC", "cluster")]
sig.res <- sig.res[!(sig.res$HGNC == "" | is.na(sig.res$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res <- sig.res[]
for(cluster in unique(sig.res$cluster)){
print(paste("writing cluster", cluster))
new.table <- sig.res[sig.res$cluster == cluster, c("HGNC", "avg_log2FC")]
new.table <- new.table[order(-new.table$avg_log2FC), ]
dir.create(paste0("RankList_res", object.res, "_findAll_hgnc/"), showWarnings = FALSE)
write.table(new.table, file = paste0("RankList_res", object.res, "_findAll_hgnc/res", object.res, "cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
}calculate FindMarkers() that distinguish each cluster (might overlab between clusters)
# ident.list <- colnames(seurat.object@meta.data)[grepl("^RNA_snn", colnames(seurat.object@meta.data))]
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1")
for(tested.ident in ident.list){
for (cluster in sort(as.numeric(levels(seurat.object@meta.data[[tested.ident]])))){
cluster.markers <- FindMarkers(seurat.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_dim", ndims, gsub("RNA_snn_", "", tested.ident), ".xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}
}for (cluster in sort(as.numeric(levels(seurat.object@meta.data[paste0("RNA_snn_res", object.res)])))){
cluster.markers <- FindMarkers(seurat.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_dim", ndims, "res", object.res, ".xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}Notes on cluster stability
Cluster stability could be influenced by: * cells in each population (cellranger v6 includes more cells than cellranger v1, especially in MEP) * dimensionality is incorrect * ScaleData didnt account for regression factors (e.g., “nCounts_RNA” or “nFeatures_RNA”) * Did not consider cell cycle * Incorrect normalization/scaling method * Clustering is too strict or not strict enough * neighborhood analysis used wrong parameters * Should include mitoC filter (there’s a chunk of MEP w/ mitoC @ ~40%) * SCTransform accounts better for sources of variability
---
title: "msAggr_seurat"
output: 
  html_notebook: 
    fig_height: 6
---

Creating new pipeline using seurat v4.0.2 available 2021.06.23  
Important notes:

* FILTERING on `percent.mt`, but NOT regressing on `percent.mt`
* Regressing on `nCounts_RNA` and `nFeature_RNA`

# Setup
## Load libraries required for Seuratv4
```{r setup, message=FALSE, warning=FALSE}
knitr::opts_knit$set(root.dir = "~/Desktop/10XGenomicsData/msAggr_scRNASeq/IndividualPops/")
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(clustree)
```
## Source functions
```{r}
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/read_10XGenomics_data.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/PercentVariance.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/ColorPalette.R")
source("~/Desktop/10XGenomicsData/msAggr_scRNASeq/RFunctions/Mouse2Human_idconversion.R")
```


# A note about using SCTransform versus `ScaleData`
https://bioconductor.org/packages/3.10/workflows/vignettes/simpleSingleCell/inst/doc/batch.html#62_for_gene-based_analyses
>You can also normalize and scale data for the RNA assay. There are numerous resources on this, but Aaron Lun describes why the original log-normalized values should be used for DE and visualizations of expression quite well here:
>
>For gene-based procedures like differential expression (DE) analyses or gene network construction, it is desirable to use the original log-expression values or counts. The corrected values are only used to obtain cell-level results such as clusters or trajectories. Batch effects are handled explicitly using blocking terms or via a meta-analysis across batches. We do not use the corrected values directly in gene-based analyses, for various reasons:
>
>It is usually inappropriate to perform DE analyses on batch-corrected values, due to the failure to model the uncertainty of the correction. This usually results in loss of type I error control, i.e., more false positives than expected.
>
>The correction does not preserve the mean-variance relationship. Applications of common DE methods like edgeR or limma are unlikely to be valid.
>
>Batch correction may (correctly) remove biological differences between batches in the course of mapping all cells onto a common coordinate system. Returning to the uncorrected expression values provides an opportunity for detecting such differences if they are of interest. Conversely, if the batch correction made a mistake, the use of the uncorrected expression values provides an important sanity check.
>
>In addition, the normalized values in SCT and integrated assays don't necessary correspond to per-gene expression values anyway, rather containing residuals (in the case of the scale.data slot for each).
## Set global variables

```{r}
projectName <- "LSKSubpop"
jackstraw.dim <- 40
```

## Store session info
```{r}
sink(paste0(projectName, "_sessionInfo.txt"))
sessionInfo()
sink()
```


```{r warning=FALSE}
setwd("~/Desktop/10XGenomicsData/cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = c("LSKm2"))
data.object<-Read10X(data_file.list)
```



```{r}
seurat.object<- CreateSeuratObject(counts = data.object, min.cells = 3, min.genes = 200, project = projectName)
```

Clean up to free memory

```{r}
remove(data.object)
```


Add mitochondrial metadata and plot some basic features
```{r}
seurat.object[["percent.mt"]] <- PercentageFeatureSet(seurat.object, pattern = "^mt-")
VlnPlot(seurat.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
```


```{r fig.width=4, fig.height=2}
plot1 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(seurat.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
```


remove low quality cells
require: nFeature_RNA between 200 and 4000 (inclusive)
require: percent.mt <= 5

```{r}
print(paste("original object:", nrow(seurat.object@meta.data), "cells", sep = " "))
seurat.object <- subset(seurat.object, 
												subset = nFeature_RNA >=200 & 
													nFeature_RNA <= 4000 & 
													percent.mt <= 5
)
print(paste("new object:", nrow(seurat.object@meta.data), "cells", sep = " "))
```



## Normalization

Struggling to wrap my head around this one. It seems that SCTransform is best for batch correction, but `NormalizeData` and `ScaleData` are best for DGE. Several vignettes have performed both

`selection.method	
How to choose top variable features. Choose one of :

vst: First, fits a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then standardizes the feature values using the observed mean and expected variance (given by the fitted line). Feature variance is then calculated on the standardized values after clipping to a maximum (see clip.max parameter).

mean.var.plot (mvp): First, uses a function to calculate average expression (mean.function) and dispersion (dispersion.function) for each feature. Next, divides features into num.bin (deafult 20) bins based on their average expression, and calculates z-scores for dispersion within each bin. The purpose of this is to identify variable features while controlling for the strong relationship between variability and average expression.

dispersion (disp): selects the genes with the highest dispersion values`




```{r}
seurat.object <- NormalizeData(seurat.object, normalization.method = "LogNormalize", scale.factor = 10000)
```





Find variable features
```{r fig.width = 5, fig.height = 2}
seurat.object <- FindVariableFeatures(seurat.object, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(seurat.object), 10)
plot1 <- VariableFeaturePlot(seurat.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
```

Scale data (linear transformation)

```{r}
all.genes <- rownames(seurat.object)
seurat.object <- ScaleData(seurat.object, features = all.genes, vars.to.regress = c("nCount_RNA", "nFeature_RNA"))
```


### Save progress

```{r}
# save.image(file = paste0(projectName, '.RData'))
saveRDS(seurat.object, file = paste0(projectName, "_raw.RDS"))
```


## PCA

linear dimensional reduction. Default are based on VariableFeatures, but can be changed

```{r}
seurat.object <- RunPCA(seurat.object, features = VariableFeatures(object = seurat.object))
```
Plot results
```{r fig.width=4, fig.height=4}
VizDimLoadings(seurat.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 2)
```

DimPlot colored by orig.ident
```{r}
DimPlot(seurat.object, reduction = "pca", group.by = "orig.ident")
```
Let's put in a concerted effort to pick the right dimensionality using the newest software
```{r}
# jackstraw.dim <- 40
# seurat.object <- JackStraw(seurat.object, num.replicate = 100, dims = jackstraw.dim) #runs ~50 min
# seurat.object <- ScoreJackStraw(seurat.object, dims = 1:jackstraw.dim)
# save.image(paste0(projectName, ".RData"))
```
Draw dim.reduction plots
```{r}
# JackStrawPlot(seurat.object, dims = 25:36)
```
```{r, figures-side, fig.show='hold', out.width="50%"}
ElbowPlot(seurat.object, ndims = 50)
percent.variance(seurat.object@reductions$pca@stdev)
```
Number of PCs describing X% of variance
```{r}
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance: ", length(which(cumsum(tot.var) <= 80)))
paste0("Num pcs for 85% variance: ", length(which(cumsum(tot.var) <= 85)))
paste0("Num pcs for 90% variance: ", length(which(cumsum(tot.var) <= 90)))
paste0("Num pcs for 95% variance: ", length(which(cumsum(tot.var) <= 95)))

```

Percent of PCs describing X% of variance (transcribed from above cell because I don't know how to freeze results)

Num pcs for 80% variance: 12
Num pcs for 85% variance: 18
Num pcs for 90% variance: 26
Num pcs for 95% variance: 37

# ID clusters based on different variances
## Describe 80% of variance with 12 dimensions

### Neighborhood and umap
set total.var <- 80%
```{r}
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 80))

seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	seurat.object <- FindClusters(seurat.object, resolution = x)
}
```

```{r}
for (meta.col in colnames(seurat.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(seurat.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
		) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

```{r}
saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

### Evaluate cluster stability
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```



```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```


```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```



```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```













## Describe 85% of variance with 18 dimensions

### Neighborhood and umap
set total.var <- 85%
```{r}
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))

seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	seurat.object <- FindClusters(seurat.object, resolution = x)
}
```

```{r}
for (meta.col in colnames(seurat.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(seurat.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
		) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

```{r}
saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

Generate statistics for each cluster/resolution combo
```{r}
current_res <- 'RNA_snn_res.0.5'

cluster_ids <- sort(unique(seurat.object@meta.data[,current_res]))
counts_df <- data.frame(matrix(nrow = length(cluster_ids), ncol = 4))
rownames(counts_df) <- cluster_ids
colnames(counts_df) <- c("LSKm2", "CMPm2", "MEPm", "GMPm")
for(id in cluster_ids){
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "LSKm2"),])
	counts_df[id, "LSKm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "LSKm2"),])
	counts_df[id, "CMPm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "MEPm"),])
	counts_df[id, "MEPm"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "GMPm"),])
	counts_df[id, "GMPm"] = cell_value
	
}

```


### Evaluate cluster stability
Must ensure we have the right cluster stability, that is, cells that start in the same cluster tend to stay in the same cluster. If your data is over-clustered, cells will bounce between groups.

Following [this tutorial by Matt O.].https://towardsdatascience.com/10-tips-for-choosing-the-optimal-number-of-clusters-277e93d72d92.
Previously my favourite has been Clustree, which gives a nice visual
NB: For some reason `clustree::clustree()` didn't work, whereas `library(clustree)` followed by `clustree()` did.

```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```






## Describe 90% of variance with 26 dimensions

### Neighborhood and umap
```{r}
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	seurat.object <- FindClusters(seurat.object, resolution = x)
}
```

```{r}
for (meta.col in colnames(seurat.object@meta.data)){
	if(grepl(pattern = "RNA_snn_res", x = meta.col)==TRUE | grepl(pattern = "orig.ident", x = meta.col)==TRUE){
		myplot <- DimPlot(seurat.object, 
											group.by = meta.col,
											reduction = "umap", 
											pt.size = 1,
											cols = color.palette) + 
			ggtitle(paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col) ))
		plot(myplot)
		png(filename = paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col), "-umap.png"), height = 800, width = 800)
		plot(myplot)
		dev.off()
		myplot <- DimPlot(seurat.object, 
											group.by = meta.col,
											reduction = "umap", 
											pt.size = 1,
											cols = color.palette) + 
			facet_wrap(meta.col) + 
			ggtitle(paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col)))
		
		png(filename = paste0(projectName, " dim", ndims, "res.", gsub("RNA_snn_res.", "", meta.col), "-umap_FacetRes.png"), height = 800, width = 800)
		plot(myplot)
		dev.off()
		
	}
}
```



```{r}
saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

Generate statistics for each cluster/resolution combo
```{r}
current_res <- 'RNA_snn_res.1'

cluster_ids <- sort(unique(seurat.object@meta.data[,current_res]))
counts_df <- data.frame(matrix(nrow = length(cluster_ids), ncol = 9))
rownames(counts_df) <- cluster_ids
colnames(counts_df) <- c("LSKm2", "CMPm2", "MEPm", "GMPm", "TotInClust", "PctClusLSK", "PctClusCMP", "PctClusMEP", "PctClusGMP")
for(id in cluster_ids){
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "LSKm2"),])
	counts_df[id, "LSKm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "CMPm2"),])
	counts_df[id, "CMPm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "MEPm"),])
	counts_df[id, "MEPm"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "GMPm"),])
	counts_df[id, "GMPm"] = cell_value
	
}
counts_df["TotInClust"] <- rowSums(counts_df, na.rm = TRUE)
counts_df$PctClusLSK <- round(counts_df$LSKm2/counts_df$TotInClust, 3)*100
counts_df$PctClusCMP <- round(counts_df$CMPm2/counts_df$TotInClust, 3)*100
counts_df$PctClusMEP <- round(counts_df$MEPm/counts_df$TotInClust, 3)*100
counts_df$PctClusGMP <- round(counts_df$GMPm/counts_df$TotInClust, 3)*100
xlsx::write.xlsx(x = counts_df, 
								 file = paste0(projectName, "CellCountPerClust",ndims, "res.1.xlsx"), 
								 sheetName = "res1", 
								 col.names = TRUE, 
								 row.names = TRUE, 
								 append = TRUE)


counts_df

```


### Evaluate cluster stability
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
png(filename = paste0(projectName, "_dim", ndims, "-clustree.png"), height = 800, width = 1600)
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
dev.off()
```
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```
## Describe 95% of variance with 37 dimensions

### Neighborhood and umap
```{r}
tot.var <- percent.variance(seurat.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))

seurat.object <- FindNeighbors(seurat.object, dims = 1:ndims)
seurat.object <- FindClusters(seurat.object, resolution = 0.5)
seurat.object <- RunUMAP(seurat.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	seurat.object <- FindClusters(seurat.object, resolution = x)
}
```

```{r}
for (meta.col in colnames(seurat.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(seurat.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
		) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

```{r}
saveRDS(seurat.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

Generate statistics for each cluster/resolution combo
```{r}
current_res <- 'RNA_snn_res.0.5'

cluster_ids <- sort(unique(seurat.object@meta.data[,current_res]))
counts_df <- data.frame(matrix(nrow = length(cluster_ids), ncol = 4))
rownames(counts_df) <- cluster_ids
colnames(counts_df) <- c("LSKm2", "CMPm2", "MEPm", "GMPm")
for(id in cluster_ids){
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "LSKm2"),])
	counts_df[id, "LSKm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "LSKm2"),])
	counts_df[id, "CMPm2"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "MEPm"),])
	counts_df[id, "MEPm"] = cell_value
	cell_value <- nrow(seurat.object@meta.data[(seurat.object@meta.data[current_res] == id) & 
																						 	(seurat.object@meta.data$orig.ident == "GMPm"),])
	counts_df[id, "GMPm"] = cell_value
	
}

```


### Evaluate cluster stability
```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```



```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```


```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'scale.data', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```



```{r fig.height=5}
clustree(seurat.object, prefix = "RNA_snn_res.", expres = 'counts', node_colour = "sc3_stability") + 
	scale_color_continuous(low = 'red3', high = 'white')
```














# DGE
I think clusters from 90% variance at 0.5 and 1.0 resolution are most stable. We'll do some statistics and DGE on that. Will also need to go back and play with SCTransform, since these are multiple cell types from multiple lanes.
## Load favourite dim reduction file
```{r}
rds.file <- "msAggr_seurat_dim26.RDS"
seurat.object <- readRDS(rds.file)
ndims <- as.numeric(gsub("[^0-9]", "", stringr::str_split(rds.file, "_")[[1]][3]))
```

## Set favourite resolution
```{r}
object.res <- ".0.5"
Idents(seurat.object) <- paste0("RNA_snn_res", object.res)
length(levels(seurat.object@active.ident))
```


## count number of filtered cells left from each population
```{r}
# Number of filtered cells left in each pop
sapply(c("LSKm2", "CMPm2", "MEPm", "GMPm"), function(x) (c(nrow(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x,]))))
```

```{r}
par(mfrow = c(2, 2))
for (x in c("LSKm2", "CMPm2", "MEPm", "GMPm")){
	h = hist(seurat.object@meta.data[seurat.object@meta.data$orig.ident == x, 'percent.mt'], breaks = 30, plot = FALSE)
	h$density = h$counts/sum(h$counts)*100
	plot(h,freq=FALSE, main =  paste(x, "percent mitoC"), xlab = "percent mitoC", ylab = "Frequency")
}
par(mfrow = c(1,1))
```

```{r fig.width=5}
VlnPlot(subset(seurat.object, subset = orig.ident == "MEPm"), 
				features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1, pt.size = 0, fill.by = 'ident', flip = TRUE)
```


## DGE w/ resolution = 0.5
Strt with comparing all clusters against all other clusters and write out cluster info
calculate `FindAllMarkers()` for different idents and save to new file
```{r}
# ident.list <- colnames(seurat.object@meta.data)[grepl("^RNA_snn", colnames(seurat.object@meta.data))]
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1")

for(tested.ident in ident.list){
	Idents(seurat.object) <- tested.ident
	all.markers <- FindAllMarkers(seurat.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_dim",ndims, "_allres.xlsx"), 
									 sheetName = tested.ident, 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
}
```

## Create `FindAllMarkers()` lists for GSEA
```{r}
object.res.allmarkers <- FindAllMarkers(seurat.object)
```

## Map HGNC symbols
```{r}
Mouse2HumanTable <- Mouse2Human(object.res.allmarkers$gene)

HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(object.res.allmarkers$gene, Mouse2HumanTable$MGI)])
head(object.res.allmarkers)
object.res.allmarkers$HGNC <- HGNC
tail(object.res.allmarkers)
sig.res <- object.res.allmarkers[object.res.allmarkers$p_val_adj <= 0.05, ]
sig.res <- sig.res[c("avg_log2FC", "HGNC", "cluster")]
sig.res <- sig.res[!(sig.res$HGNC == "" | is.na(sig.res$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res <- sig.res[]

```


```{r}
for(cluster in unique(sig.res$cluster)){
	print(paste("writing cluster", cluster))
	new.table <- sig.res[sig.res$cluster == cluster, c("HGNC", "avg_log2FC")]
	new.table <- new.table[order(-new.table$avg_log2FC), ]
	dir.create(paste0("RankList_res", object.res, "_findAll_hgnc/"), showWarnings = FALSE)
	write.table(new.table, file = paste0("RankList_res", object.res, "_findAll_hgnc/res", object.res, "cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
	
}
```



calculate `FindMarkers()` that distinguish each cluster (might overlab between clusters)
```{r}
# ident.list <- colnames(seurat.object@meta.data)[grepl("^RNA_snn", colnames(seurat.object@meta.data))]
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1")
for(tested.ident in ident.list){
	for (cluster in sort(as.numeric(levels(seurat.object@meta.data[[tested.ident]])))){
		cluster.markers <- FindMarkers(seurat.object, ident.1 = cluster)
		xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
										 file = paste0(projectName, "_FindMarkers_dim", ndims, gsub("RNA_snn_", "", tested.ident), ".xlsx"), 
										 sheetName = paste0("clst", cluster), 
										 col.names = TRUE, 
										 row.names = TRUE, 
										 append = TRUE)
	}
}
```



```{r}
for (cluster in sort(as.numeric(levels(seurat.object@meta.data[paste0("RNA_snn_res", object.res)])))){
	cluster.markers <- FindMarkers(seurat.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_dim", ndims, "res", object.res, ".xlsx"), 
									 sheetName = paste0("clst", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}
```


# Notes on cluster stability
Cluster stability could be influenced by:
* cells in each population (cellranger v6 includes more cells than cellranger v1, especially in MEP)
* dimensionality is incorrect
* ScaleData didnt account for regression factors (e.g., "nCounts_RNA" or "nFeatures_RNA")
* Did not consider cell cycle
* Incorrect normalization/scaling method
* Clustering is too strict or not strict enough
* neighborhood analysis used wrong parameters
* Should include mitoC filter (there's a chunk of MEP w/ mitoC @ ~40%)
* SCTransform accounts better for sources of variability
